Building Multimodal AI Applications with Pixeltable: The Data Infrastructure for the AI Era

Modern AI applications increasingly need to handle multiple types of data—text, images, video, and audio—seamlessly and efficiently. However, managing these different modalities typically requires juggling multiple specialized tools and writing complex integration code. Today, we’ll explore how Pixeltable provides a unified, declarative interface for building multimodal AI applications while maintaining production-grade performance and reliability.

The Multimodal Challenge

Consider a typical multimodal application pipeline:

• Extract audio from videos
• Transcribe speech to text
• Generate embeddings for search
• Maintain cross-modal relationships
• Update models and indexes efficiently

Traditional approaches often look like this:

# Traditional approach - multiple tools and complex orchestration
def process_content(video_path):
# Extract audio
    video = VideoFileClip(video_path)
    audio = video.audio
    audio.write_audiofile("temp.mp3")

# Transcribe
    client = OpenAI()
    transcript = client.audio.transcribe("temp.mp3")

# Generate embeddings
    embeddings = model.encode(transcript)

# Store results...# Handle updates...# Manage relationships...

The Pixeltable Solution: Unified Multimodal Processing

# Create a unified table for multimodal content
content = pxt.create_table('content_table', {
    'video': pxt.VideoType(),
    'metadata': pxt.JsonType()
})

# Audio extraction and transcription as computed columns
content['audio'] = video.extract_audio(content_table.video)
content['transcript'] = openai.transcriptions(content_table.audio, model='whisper-1')

# To index transcriptions, we split them into sentences.
sentences_view = pxt.create_view(
    'sentences_view',
    content_table,
    iterator=StringSplitter.create(
        text=content_table.transcription.text,
        separators='sentence'
    )
)

# Add search capabilities
sentences_view.add_embedding_index('text', string_embed=e5_embed)

Key Features and Benefits

1. Unified Data Management
   
   
   
   • Single interface for all modalities
   • Automatic format handling
   • Built-in type safety

# Seamlessly handle multiple types
content.insert([
    {'video': 'path/to/video.mp4'},
    {'audio': 'path/to/audio.mp3'},
    {'image': 'path/to/image.jpg'}
])

2. Cross-Modal Processing
   
   
   
   • Automatic format conversions
   • Maintain relationships between modalities
   • Efficient resource utilization

# Cross-modal search example
text_query = "person walking on beach"
similar_videos = content.order_by(
    content.video_embedding.similarity(clip_text(text_query)),
    asc=False
).limit(5)

1. Advanced Search Capabilities
   
   
   
   • Multi-modal similarity search
   • Hybrid ranking
   • Efficient index updates

# This creates a reusable query that can be attached to a table/view. 
@messages_view.query
   def get_context(question_text: str):
       sim = messages_view.text.similarity(question_text)
             return (
              messages_view
              .where(sim > 0.3)
              .order_by(sim, asc=False)
              .select(
                 text=messages_view.text,
                 username=messages_view.username,
                 sim=sim
              )
              .limit(20)
            )

This pattern is commonly used in RAG (Retrieval Augmented Generation) applications where you want to:

• Find relevant context for each question
• Include this context when generating responses
• Keep track of which messages were used as context for each question

The advantage of implementing this as a computed column is that:

• The retrieval happens automatically for each new question
• The results are stored and versioned alongside the questions
• The entire process is declarative and easily reproducible
• You get automatic incremental updates if new messages are added to messages_view

This is much cleaner than implementing the same functionality with separate data processing pipelines or manual orchestration.

See It In Action

• Demo: Multimodal Powerhouse
• Tutorial: Audio Transcription and Indexing
• Standalone App: Image/Text Similarity Search

Getting Started

import pixeltable as pxt

# Create a multimodal table
content = pxt.create_table('content', {
    'video': pxt.VideoType(),
    'text': pxt.StringType(),
    'metadata': pxt.JsonType()
})

Building multimodal AI applications doesn’t have to mean juggling multiple tools and writing complex integration code. With Pixeltable’s unified approach, you can focus on building innovative applications while we handle the infrastructure complexity. Start building your multimodal AI applications with Pixeltable today.